wfgy-formula-all.md

tools	read write edit grep bash
arguments	$QUERY

WFGY Formula All - Complete Reasoning Pipeline

Apply all four WFGY formula modules (BBMC, BBPF, BBCR, BBAM) in sequence for comprehensive semantic reasoning.

Based on the WFGY project: https://github.com/onestardao/WFGY

Module Pipeline

1. BBMC - Semantic Residue Minimization
2. BBPF - Multi-Path Progression
3. BBCR - Collapse-Rebirth Correction (if needed)
4. BBAM - Attention Modulation

Instructions

1. Initialize Pipeline

   • Parse input query: "$QUERY"
   • Load system configuration from .wfgy/config.json
   • Check all modules are initialized
   • Create pipeline session in .wfgy/sessions/
   • Set up monitoring for each module

2. Phase 1: BBMC (Minimization)

   • Calculate initial semantic residue
   • Apply formula: B = I - G + m * c²
   • Minimize ||B||₂ through iteration
   • Record residue metrics
   • Pass optimized state to BBPF

3. Phase 2: BBPF (Multi-Path)

   • Generate N parallel reasoning paths
   • Apply: x_next = x + Σ V_i + Σ(W_j * P_j)
   • Weight and rank paths by probability
   • Select top paths for synthesis
   • Check for divergence requiring BBCR

4. Phase 3: BBCR (Correction)

   • Monitor for collapse conditions:
     • ||B|| ≥ 0.85 (high residue)
     • f(S) < 0.01 (stalled progression)
     • Logic contradictions detected
   • If triggered:
     • Execute collapse-reset-rebirth cycle
     • Preserve valid components
     • Return to stable state
   • Continue to BBAM

5. Phase 4: BBAM (Attention)

   • Analyze attention distribution
   • Apply: â_i = a_i * exp(-0.618 * std(a))
   • Focus on critical elements
   • Balance attention across concerns
   • Generate final optimized output

6. Integration and Output

   • Synthesize results from all modules
   • Calculate overall metrics:
     • Total semantic tension (ΔS)
     • Logic vector direction (λ)
     • Stability score
     • Confidence level
   • Create semantic node if recording active
   • Generate comprehensive report

Output Format

WFGY Complete Formula Analysis
═══════════════════════════════════════
Query: "$QUERY"

Module Results:
┌────────────────────────────────────────┐
│ Module │ Status    │ Key Metric        │
├────────────────────────────────────────┤
│ BBMC   │ ✓ Applied │ Residue: 0.32     │
│ BBPF   │ ✓ Applied │ Paths: 5          │
│ BBCR   │ ○ Skipped │ Stable            │
│ BBAM   │ ✓ Applied │ Focus: 0.78       │
└────────────────────────────────────────┘

Semantic Analysis:
- Tension (ΔS): [value]
- Logic Vector (λ): [direction]
- Confidence: [percentage]%
- Risk Zone: [Safe/Transitional/Risk/Danger]

Primary Reasoning Path:
[Main conclusion from analysis]

Alternative Paths (from BBPF):
1. [Alternative approach]
2. [Alternative approach]

Key Focus Points (from BBAM):
• [Critical element 1]
• [Critical element 2]
• [Critical element 3]

Optimized Response:
[Final synthesized reasoning with all optimizations applied]

Semantic Node Created: [Yes/No]
Node ID: [if created]

Performance Metrics

Expected improvements over baseline:

• Reasoning accuracy: +22.4%
• Chain validity: +42.1%
• Stability: 3.6× increase
• Hallucination events: Significantly reduced

Advanced Options

# Run with custom parameters
/wfgy:formula-all "query" --paths 10 --gamma 0.5 --threshold 0.7

# Run with verbose logging
/wfgy:formula-all "query" --verbose

# Run with specific module emphasis
/wfgy:formula-all "query" --emphasize bbpf

Use Cases

• Complex multi-step reasoning tasks
• Problems requiring both creativity and precision
• Situations with high uncertainty
• Tasks needing attention optimization
• Full semantic analysis of concepts

Integration

After running formula-all:

• /semantic:node-build to record the complete analysis
• /semantic:tree-view to see reasoning history
• /boundary:detect to verify knowledge boundaries
• /reasoning:chain-validate to confirm logic consistency